model.frame               package:base               R Documentation

_E_x_t_r_a_c_t_i_n_g _t_h_e "_E_n_v_i_r_o_n_m_e_n_t" _o_f _a _M_o_d_e_l _F_o_r_m_u_l_a

_D_e_s_c_r_i_p_t_i_o_n:

     'model.frame' (a generic function) and its methods return a
     'data.frame' with the variables needed to use 'formula' and any
     '...' arguments.

_U_s_a_g_e:

     model.frame(formula, ...)

     ## Default S3 method:
     model.frame(formula, data = NULL,
                subset = NULL, na.action = na.fail,
                drop.unused.levels = FALSE, xlev = NULL, ...)

     ## S3 method for class 'aovlist':
     model.frame(formula, data = NULL, ...)

     ## S3 method for class 'glm':
     model.frame(formula, data, na.action, ...)

     ## S3 method for class 'lm':
     model.frame(formula, data, na.action, ...)

_A_r_g_u_m_e_n_t_s:

 formula: a model formula

    data: 'data.frame', list, 'environment' or object coercible to
          'data.frame' containing the variables in 'formula'.

  subset: a specification of the rows to be used: defaults to all rows.
          This can be any valid indexing vector (see '[.data.frame' for
          the rows of 'data' or if that is not supplied, a data frame
          made up of the variables used in 'formula'.

na.action: how 'NA's are treated. The default is first, any 'na.action'
          attribute of 'data', second a 'na.action' setting of
          'options', and third 'na.fail' if that is unset.  The
          "factory-fresh" default is 'na.omit'.

drop.unused.levels: should factors have unused levels dropped? Defaults
          to 'FALSE'.

    xlev: a named list of character vectors giving the full set of
          levels to be assumed for each factor.

     ...: further arguments such as 'subset', 'offset' and 'weights'.
          'NULL' arguments are treated as missing.

_D_e_t_a_i_l_s:

     Variables in the formula, 'subset' and in '...' are looked for
     first in 'data' and then in the environment of 'formula': see the
     help for 'formula()' for further details.

     First all the variables needed are collected into a data frame.
     Then 'subset' expression is evaluated, and it is is used as a row
     index to the data frame. Then the 'na.action' function is applied
     to the data frame (and may well add attributes).  The levels of
     any factors in the data frame are adjusted according to the
     'drop.unused.levels' and 'xlev' arguments.

_V_a_l_u_e:

     A 'data.frame' containing the variables used in 'formula' plus
     those specified '...'.

_R_e_f_e_r_e_n_c_e_s:

     Chambers, J. M. (1992) _Data for models._ Chapter 3 of
     _Statistical Models in S_ eds J. M. Chambers and T. J. Hastie,
     Wadsworth & Brooks/Cole.

_S_e_e _A_l_s_o:

     'model.matrix' for the "design matrix", 'formula' for formulas 
     and 'expand.model.frame' for model.frame manipulation.

_E_x_a_m_p_l_e_s:

     data(cars)
     data.class(model.frame(dist ~ speed, data = cars))

